Is Punishing Friends Effective? An Analysis of Labors Withdrawal of - PowerPoint PPT Presentation

Is Punishing Friends Effective? An Analysis of Labor’s Withdrawal of Campaign Funds from Pro-Free Trade Democrats Joshua Jansa Michele Hoyman Blake Whitney Oklahoma State University University of North Carolina Oklahoma State University joshua.jansa@okstate.edu hoyman@unc.edu blake.whitney@okstate.edu

Organized Labor’s Political Philosophy “Reward your friends, punish your enemies” – Samuel Gompers, AFL President, 1898 • Build relationships within two-party system • Mobilize resources for Democratic allies

Tension on Trade • Clinton and the New Democrats: pro- free-trade • Unions: against NAFTA and subsequent free-trade bills

Incorporating Punishment Rhetorical Evidence “On this issue, just because there’s a ‘D’ after your name doesn’t mean you’ll automatically get our support.” – Alan Reuther, Chief Lobbyist for UAW, after PNTR vote in 2000 Empirical Evidence • Punishment for pro-NAFTA and pro-PNTR Dems (Jackson & Engel 1998; 2003) • Industrial unions withheld $7,200 on average from pro-free-trade Dems over 12-year period (Jansa & Hoyman 2017)

Research Question • No study has looked at the effectiveness of punishment • We ask: Has punishment been effective in moving Democratic allies from pro-free-trade to anti-free-trade positions?

Competing Hypotheses • Punishment could be effective • It signals controversy introduces uncertainty • H1a: If a legislator experiences a decrease in contributions from labor PACs, she will be more likely to change her vote from pro- to anti-free trade in the subsequent session of Congress. • Punishment could be ineffective • It is an unwelcome tactic that can erode trust and access • H1b: If a legislator experiences a decrease in contributions from labor PACs, she will be less likely to change her vote from pro- to anti-free trade in the subsequent session of Congress.

Rewards as an Alternative Strategy • Rewards subsidize costly behavior, like vote-switching • H2: If a legislator receives an increase in contributions from labor PACs, she will be more likely to change her vote from pro- to anti-free trade.

Dependent Variable • Switch to Anti-Free Trade : 1 if legislator changed from supporting at least one free-trade bill in the previous session of Congress to voting against all free-trade bills; 0 otherwise. • Data: 13 key trade votes scored by the AFL-CIO from 1996-2008 • Example: if a legislator voted for Chile FTA or Singapore FTA in the 108 th Congress, but against both CAFTA and Oman FTA in the 109 th Congress, then they received a 1.

Vote-Switching on Trade, 1996-2008 X-axis is the 0 number of legislators in each category. 1 Y-axis in the frequency of vote switching. 2 3 0 20 40 60 80 100 120 140

Key Independent Variables • Punishment by labor PACs • Two measures: dichotomous and total withheld (in $10,000s) • Data: Center for Responsive Politics • Timing: Punishment in previous session ( t = -1 ) used to predict votes in current session ( t = 0 ) • Rewards by labor PACs • Two measures: dichotomous and total increase (in $10,000s) • Data: Center for Responsive Politics • Timing: Rewards in current session ( t = 0 ) used to predict votes in current session ( t = 0 )

Control Variables and Model Choice • Rewards from business PACs • Ideological extremism, state-level union density (%), district-level manufacturing employment (%), leadership, seniority, close election. • Panel logit with random effects • Standard errors clustered by legislator

Estimates of Reward and Punishment on Vote Switching, Dichotomous Measures Key findings: House Democrats less likely to • switch vote when punished Unintended effect • House Democrats more likely • to switch when rewarded House Democrats less likely to • switch when rewarded by business PACs Coefficient Estimate

Estimates of Reward and Punishment on Vote Switching, Total change (in $10,000s) Key findings: Effect of punishment size • indistinguishable from zero House Democrats more • likely to switch when rewarded House Democrats less likely • to switch when rewarded by business PACs Coefficient Estimate

Change in Probability of Switching to Anti-Free-Trade For $10,000 in additional labor contributions, Dems were 4% more likely to change their free trade voting record. For $10,000 in additional business contributions, Dems were 2% less likely to change their free trade voting record. From the minimum labor reward ($100) to maximum ($24,000), a 9.6% increase in the probability of switching. From the minimum business reward ($500) to maximum ($140,000), a 27.9% decrease in the probability of switching.

Implications • Punishment strategy backfires • Logical strategy, but ineffective • Labor should favor of rewards, though limited due to business advantage • Waning influence perhaps due to choice of tactics • Opting for punishment over reward • Playing the money game, instead of grassroots strategy

Thank you! Questions?

Why Punish?: Exit, Voice, Loyalty • Exit: swing support to Republican candidates • “Encourages competition by both parties for labor support” (Dark 2003) • Not viable without “concessions” from Republicans (Bok & Dunlop 1970) • Voice: signal displeasure via punishment • “[Labor] wanted the members to win re -election but get back in line when they returned to Congress” (Engel and Jackson 2003) • Risks reduced trust and access; backlash (Jansa & Hoyman 2018) • Loyalty: do nothing

Select Trade Votes, 1993-2000 Democrats Democrats Voting For Voting Against Congre Votes AFL-CIO AFL-CIO ss Position Position 103 rd NAFTA (1993) 102 156 GATT (1994) 89 167 China MFN (1994) 111 145 104 th China MFN (1996) 75 119 106 th Ban on PNTR with China 98 110 (1999) PNTR with China (2000) 138 73

Model 1 Variables -0.583** Punished by Labor (0.199) Full Model 0.885*** Rewarded by Labor Results (0.212) -1.904*** Dichotomous measures Rewarded by Business (0.293) Constant not shown -3.185*** Ideological Extremism (0.849) 0.035* Union Density (0.014) -0.060** Manufacturing (0.018) 0.657 Leadership (0.383) -0.056* Seniority (0.024) 0.278 Close Election (0.341) N observations 837 BIC 766.49

Model 2 Variables -0.070 Full Model Results Punishment Size (in $10,000s) (0.055) 0.177*** Reward Size, Labor (in $10,000s) Total Changes (in $10,000s) (0.041) -0.072*** Constant not shown Reward Size, Business (in $10,000s) (0.016) -3.377*** Ideological Extremism (0.790) 0.031* Union Density (0.013) -0.046* Manufacturing (0.020) 1.624 Leadership (0.860) -0.047 Seniority (0.026) 0.208 Close Election (0.349) N observations 837 BIC 800.28

Results with Variables Measured at Alternative Times